Number of cluters of Kmedoids
Kmedoids_clusterN(dt)
## Loading required package: Matrix
##
## Attaching package: 'Matrix'
## The following objects are masked from 'package:tidyr':
##
## expand, pack, unpack
Kmedoids_clusterN(dt, cluster = "Kmedoids-dr")
Kmedoids_gap(dt)
gap <- Kmedoids_gap(dt)
gap %>% group_by(representor, n_gap) %>% count()
gap %>% group_by(representor, n_gap, n_cluster) %>% count()
visualizeDistance(dt_orig, "ts-dr", "euclidean", "Kmedoids-dr")
inspect_silhouette(dt_orig, "ts-dr")
## Silhouette of 98 units in 2 clusters from pam(x = distance_mat, k = nl$other[[idx]]$n_cluster, diss = TRUE) :
## Cluster sizes and average silhouette widths:
## 41 57
## 0.2085589 0.3485292
## Individual silhouette widths:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.0196 0.1652 0.2863 0.2900 0.4040 0.5502
## Silhouette of 98 units in 5 clusters from pam(x = distance_mat, k = nl$other[[idx]]$gap, diss = TRUE) :
## Cluster sizes and average silhouette widths:
## 14 25 17 20 22
## 0.2101185 0.3296237 0.2005931 0.0138171 0.2247882
## Individual silhouette widths:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.27070 0.08895 0.21794 0.20218 0.32793 0.50213
visualizeDistance(dt_orig, "error-dr", "euclidean", "Kmedoids-dr")
inspect_silhouette(dt_orig, "error-dr")
## Silhouette of 98 units in 28 clusters from pam(x = distance_mat, k = nl$other[[idx]]$n_cluster, diss = TRUE) :
## Cluster sizes and average silhouette widths:
## 3 6 6 2 6 2
## 0.053431497 0.175802544 -0.018311458 0.209598984 -0.008925677 0.480756257
## 3 3 4 4 13 3
## 0.170764861 0.001260152 0.051239700 0.264549625 0.454430501 0.319260902
## 1 3 1 3 7 3
## 0.000000000 0.176022478 0.000000000 0.131458346 -0.026089228 0.282133661
## 6 3 1 2 4 2
## 0.640451563 0.265860729 0.000000000 0.875258185 0.200404800 0.797274173
## 1 1 3 2
## 0.000000000 0.000000000 0.363882808 0.898451930
## Individual silhouette widths:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.15127 0.02426 0.16280 0.24835 0.44051 0.89959
## Silhouette of 98 units in 2 clusters from pam(x = distance_mat, k = nl$other[[idx]]$gap, diss = TRUE) :
## Cluster sizes and average silhouette widths:
## 53 45
## 0.01100589 0.28603497
## Individual silhouette widths:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.17119 0.02509 0.11531 0.13729 0.26949 0.44630
visualizeDistance(dt_orig, "accuracy", "euclidean")
Group Visualize
visualizeGroup(dt_orig, "error-dr", "euclidean", cluster= "Kmedoids-dr",names = dt_names)
visualizeGroup(dt_orig, "ts-dr", "euclidean", cluster= "Kmedoids-dr",names = dt_names)
visualizeGroup(dt_orig, "ts.features-dr", "euclidean", cluster= "Kmedoids-dr",names = dt_names)
visualizeGroup(dt_orig, "error.features-dr", "euclidean", cluster= "Kmedoids-dr",names = dt_names)
Overall statistics
avg_measure_fn(dt, metric = "rmsse") %>% arrange(bottom)
Overall rank mcb test
rank_compare(dt, filter_random = TRUE)
## Warning: Using an external vector in selections was deprecated in tidyselect 1.1.0.
## ℹ Please use `all_of()` or `any_of()` instead.
## # Was:
## data %>% select(measure)
##
## # Now:
## data %>% select(all_of(measure))
##
## See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoo
